Mistake Captioning: A Machine Learning Approach For Detecting Mistakes and Generating Instructive Feedback

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Giving feedback to students is not just about marking their answers as correct or incorrect, but also finding mistakes in their thought process that led them to that incorrect answer. In this paper, we introduce a machine learning technique for mistake captioning, a task that attempts to identify mistakes and provide feedback meant to help learners correct these mistakes. We do this by training a sequence-to-sequence network to generate this feedback based on domain experts. To evaluate this system, we explore how it can be used on a Linguistics assignment studying Grimm's Law. We show that our approach generates feedback that outperforms a baseline on a set of automated NLP metrics. In addition, we perform a series of case studies in which we examine successful and unsuccessful system outputs.

Original languageEnglish
Title of host publicationInternational Conference Recent Advances in Natural Language Processing, RANLP 2021
Subtitle of host publicationDeep Learning for Natural Language Processing Methods and Applications - Proceedings
EditorsGalia Angelova, Maria Kunilovskaya, Ruslan Mitkov, Ivelina Nikolova-Koleva
Pages1455-1462
Number of pages8
ISBN (Electronic)9789544520724
DOIs
StatePublished - 2021
EventInternational Conference on Recent Advances in Natural Language Processing: Deep Learning for Natural Language Processing Methods and Applications, RANLP 2021 - Virtual, Online
Duration: Sep 1 2021Sep 3 2021

Publication series

NameInternational Conference Recent Advances in Natural Language Processing, RANLP
ISSN (Print)1313-8502

Conference

ConferenceInternational Conference on Recent Advances in Natural Language Processing: Deep Learning for Natural Language Processing Methods and Applications, RANLP 2021
CityVirtual, Online
Period9/1/219/3/21

Bibliographical note

Publisher Copyright:
© 2021 Incoma Ltd. All rights reserved.

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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